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Analysis and predictive modeling of performance parameters in electrochemical drilling process

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Abstract

In this paper, the effect of feed rate, voltage, and flow rate of electrolyte on some performance parameters such as surface roughness, material removal rate, and over-cut of SAE-XEV-F valve-steel during electrochemical drilling in NaCl and NaNo3 electrolytic solutions have been studied using the main effect plot, the interaction plot and the ANOVA analysis. In continuation, in this case which the training dataset was small, an investigation has been done on the capability of the optimum presented regression analysis (RA), artificial neural network (ANN), and co-active neuro-fuzzy inference system (CANFIS) to predict the surface roughness, material removal rate and over-cut. The predicted parameters by the employed models have been compared with the experimental data. The comparison of results indicated that in electrochemical drilling using different electrolytic solutions, CANFIS gives the best results to predict the surface roughness and over-cut as well, while ANN is the best for predicting the material removal rate.

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References

  1. Bhattacharyya B, Malapati M, Munda J (2005) Experimental study on electrochemical micromachining. J Mater Process Technol 169:485–492

    Article  Google Scholar 

  2. ASM Handbook Committee (1988) ASM Metals Handbook. Machining, vol 16, 9th edn. ASM International, Metals Park, OH

    Google Scholar 

  3. Mahdavinejad R, Hatami M (2008) On the application of electrochemical machining for inner surface polishing of gun barrel chamber. J Mater Process Technol 202:307–315

    Article  Google Scholar 

  4. Chandrasekaran M, Muralidhar M, Murali Krishna C, Dixit US (2009) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol. doi:10.1007/s00170-009-2104-x

    Google Scholar 

  5. Benardos PG, Vosniakos GC (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43:833–844

    Article  Google Scholar 

  6. Zare Chavoshi S, Tajdari M (2010) Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool. Int J Mater Form. doi:10.1007/s12289-009-0679-2

    Google Scholar 

  7. Benardos PG, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Rob Comput Integr Manuf 18:343–354

    Article  Google Scholar 

  8. Sharma VS et al (2008) Estimation of cutting forces and surface roughness for hard turning using neural networks. J Intell Manuf 19(4):473–483

    Article  Google Scholar 

  9. Lela B, Bajić D, Jozić S (2008) Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling. Int J Adv Manuf Technol. doi:10.1007/s00170-008-1678-z

    Google Scholar 

  10. Neto JCDS, Silva EMD, Silva MBD (2006) Intervening variables in electrochemical machining. J Mater Process Technol 179:92–96

    Article  Google Scholar 

  11. Senthilkumar C, Ganesan G, Karthikeyan R (2009) Study of electrochemical machining characteristics of Al/SiCp composites. Int J Adv Manuf Technol 43:256–263

    Article  Google Scholar 

  12. Munda J, Bhattacharyya B (2008) Investigation into electrochemical micromachining (EMM) through response surface methodology based approach. Int J Adv Manuf Technol 35:821–832

    Article  Google Scholar 

  13. Sarkar BR, Doloi B, Bhattacharyya B (2006) Parametric analysis on electrochemical discharge machining of silicon nitride ceramics. Int J Adv Manuf Technol 28:873–881

    Article  Google Scholar 

  14. Bilgi DS et al (2008) Predicting radial overcut in deep holes drilled by shaped tube electrochemical machining. Int J Adv Manuf Technol 39:47–54

    Article  Google Scholar 

  15. El-Taweel TA (2008) Modelling and analysis of hybrid electrochemical turning turning magnetic abrasive finishing of 6061 Al/Al2O3 composite. Int J Adv Manuf Technol 37:705–714

    Article  Google Scholar 

  16. Asokan P et al (2008) Development of multi-objective optimization models for electrochemical machining process. Int J Adv Manuf Technol 39:55–63

    Article  Google Scholar 

  17. Kumanan S, Jesuthanam CP, Ashok Kumar R (2008) Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness. Int J Adv Manuf Technol 35:778–788

    Article  Google Scholar 

  18. Dweiri F, Al-Jarrah M, Al-Wedyan H (2003) Fuzzy surface roughness modelling of CNC down milling of Alumic-79. J Mater Process Technol 133:266–275

    Article  Google Scholar 

  19. Uros Z, Frank C, Edi K (2009) Adaptive network based inference system for estimation of flank wear in end-milling. J Mater Process Technol 209:1504–1511

    Article  Google Scholar 

  20. Caydas U, Hascalik A, Ekici S (2009) An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM. Expert Syst Appl 36:6135–6139

    Article  Google Scholar 

  21. Mat Darus IZ, Tokhi MO (2005) Soft computing-based active vibration control of a flexible structure. Eng Appl Artif Intell 18:93–114

    Article  Google Scholar 

  22. Sharma VS, Sharma SK, Sharma AK (2008) Cutting tool wear estimation for turning. J Intell Manuf 19:99–108

    Article  Google Scholar 

  23. Gölŏglu C, Arslan Y (2009) Zigzag machining surface roughness modelling using evolutionary approach. J Intell Manuf 20:203–210

    Article  Google Scholar 

  24. Tsai KM, Wang PJ (2001) Predictions on surface finish in electrical discharge machining based upon neural network models. Int J Mach Tools Manuf 41:1385–1403

    Article  Google Scholar 

  25. Wong JT, Chen KH, Su CT (2008) Designing a system for a process parameter determined through modified PSO and fuzzy neural network. PAKDD 2008, LNAI 5012. pp. 785–794

  26. Tsai KM, Wang PJ (2001) Comparisons of neural network models on material removal rate in electrical discharge machining. J Mater Process Technol 117:111–124

    Article  Google Scholar 

  27. Li X, Guan X, Li Y (2004) A hybrid radial basis function neural network for dimensional error prediction in end milling. ISNN 2004, LNCS 3174. pp. 743–748

  28. Hardalac F et al (2004) The examination of the effects of obesity on a number of arteries and body mass index by using expert systems. J Med Syst 28:129–142

    Article  Google Scholar 

  29. Dinh NQ, Afzulpurkar NV (2007) Neuro-fuzzy MIMO nonlinear control for ceramic roller kiln. Simul Model Pract Theory 15:1239–1258

    Article  Google Scholar 

  30. Saemi M, Ahmadi M (2008) Integration of genetic algorithm and a coactive neuro-fuzzy inference system for permeability prediction from well logs data. Transp Porous Media 71:273–288

    Article  Google Scholar 

  31. Mintz R, Young BR, Svrcek WY (2005) Fuzzy logic modeling of surface ozone concentrations. Comput Chem Eng 29:2049–2059

    Article  Google Scholar 

  32. Camps-Valls et al (2003) Support vector machines for crop classification using hyperspectral data. IbPRIA 2003, LNCS 2652. pp. 134–141

  33. Chen J, Roberts C, Weston P (2008) Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems. Control Eng Pract 16:585–596

    Article  Google Scholar 

  34. Aytek A (2009) Co-active neurofuzzy inference system for evapotranspiration modeling. Soft Comput 13:691–700

    Article  Google Scholar 

  35. Singh TN, Verma AK, Sharma PK (2007) A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis. Geotech Geol Eng 25:395–407

    Article  Google Scholar 

  36. Montgometry DC (2000) Design and analysis of experiment. Wiley, New York

    Google Scholar 

  37. Antony J (2003) Design of experiments for engineers and scientists. ISBN: 0750647094. Elsevier,New York

  38. Lippman RP (1987) An introduction to computing with neural nets. IEEE ASSP Mag 4:4–22

    Article  Google Scholar 

  39. Anderson D, McNeil G (1992) Artificial neural network technology. Kaman Sciences Corporation, Santa Monica, CA

    Google Scholar 

  40. Klimasauskas CC (1991) Neural computing. Neural-Ware, Pittsburgh

    Google Scholar 

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Authors and Affiliations

  1. Manufacturing Engineering Division, Department of Engineering and High-Tech, Iran University of Industries and Mines, Tehran, Iran

    Saeed Zare Chavoshi

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  1. Saeed Zare Chavoshi
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Correspondence to Saeed Zare Chavoshi.

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Zare Chavoshi, S. Analysis and predictive modeling of performance parameters in electrochemical drilling process. Int J Adv Manuf Technol 53, 1081–1101 (2011). https://doi.org/10.1007/s00170-010-2897-7

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  • DOI: https://doi.org/10.1007/s00170-010-2897-7

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